The National Academies

NCHRP 20-44(50) [Active]

Implementation of Asphalt Pavement Raveling Detection Algorithm
[ NCHRP 20-44 (NCHRP Implementation Support Program) ]

  Project Data
Funds: $225,000
Staff Responsibility: Patrick Zelinski
Research Agency: Georgia Tech Research Corporation
Principal Investigator: Yichang (James) Tsai
Effective Date: 3/25/2024
Completion Date: 3/24/2026


State departments of transportation (DOTs) use different pavement surfaces, such as open-graded friction courses (OGFC), seal coats, and chip seals. Raveling (loss of aggregates) is a predominant pavement distress that impacts the safety and functionality of the pavement surface. Some state DOTs have classified raveling into different severity levels to determine the appropriate pavement preservation actions. However, current practices for the visual inspection of the severity levels of raveling are time-consuming, labor-intensive, and, most importantly, subjective.

NCHRP IDEA 20-30/IDEA 163, "Development of an Asphalt Pavement Raveling Detection Algorithm using Emerging 3-D Laser Technology and Macrotexture Analysis," successfully developed automatic raveling detection and classification algorithms using three-dimensional (3D) pavement surface condition data, macro-texture analysis, and machine learning (ML) modeling. Different ML models were critically evaluated, and automatic raveling detection and classification algorithms were developed. The algorithms use the 3D pavement surface data already collected by state DOTs for the evaluation of cracking and rutting in pavements, so additional data collection effort is not needed. The output from the automatic raveling detection and classification algorithm is the severity level (severe/3, medium/2, and low/1) based on the 3D pavement images collected at different image sizes (e.g., 5-meter or 8-meter intervals) as specified by the different 3D sensing systems used. 

Based on the process described in NCHRP IDEA 20-30/IDEA 163, Florida DOT developed its own algorithm for classifying pavement raveling. The Florida DOT algorithm, which was written in Python, uses a random forest classifier, and the algorithm development included a training module where human raters looked at images and classified them. This algorithm can also read images, process them, and classify raveling by severity. 


The objective of this research is to develop guidelines for implementing the automatic raveling detection and classification algorithms developed in NCHRP IDEA 20-30/IDEA 163 in available programming languages or commercial software, such as Python or MATLAB. State DOTs should be able to use the products to calibrate and refine the algorithms for future use, which may include the use of different severity classifications and surface types, as well as incorporate the algorithms in their pavement management system or use the algorithms to make maintenance and rehabilitation decisions.  


Work has been initiated. The final report is expected in March 2026.

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